Feature Extraction for Polyp Detection in Wireless Capsule Endoscopy Video Frames

Abstract

Wireless capsule endoscopy (WCE) is a technology for filming the gastrointestinal (GI) tract to find abnormalities such as tumors, polyps, and bleeding. This paper proposes a new method based on hand-crafted features to detect polyps in WCE frames. A polyp has a convex surface containing pixel values with a specified Gaussian distribution. If a polyp exists in the WCE image, edges will be seen at the border of the occupied area. Since WCE images often suffer from low illumination, a histogram equalization (HE) technique can be used to enhance the image. In this paper, we initially find probable polyp edges via thresholding. Then, we use the edges to find the region of interest (ROI). Then, the mean, standard deviation (STD), and division of mean by STD from the ROI are computed as features to discriminate between polyp and nonpolyp using a support vector machine (SVM). The evaluation results on the Kvasir-Capsule dataset show 99% accuracy for the proposed method in polyp detection. Furthermore, the proposed method runs at a real-time speed of ∼0.031 seconds detection for each image.